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cs.CV2025
MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization
Chengyue Huang, Mellon M. Zhang, Robert Azarcon +2
Vision-Language-Action (VLA) models inherit strong priors from pretrained Vision-Language Models (VLMs), but naive fine-tuning often disrupts these representations and harms genera…
cs.CV2025
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering
Chengyue Huang, Brisa Maneechotesuwan, Shivang Chopra +1
Visual question answering (VQA) systems face significant challenges when adapting to real-world data shifts, especially in multi-modal contexts. While robust fine-tuning strategies…
cs.CV2025
Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models
Chengyue Huang, Yuchen Zhu, Sichen Zhu +4
Vision-language models (VLMs) are widely assumed to exhibit in-context learning (ICL), a property similar to that of their language-only counterparts. While recent work suggests VL…